Publications -> Conference Papers

In e-commerce, recommendation is an essential feature to provide users with potentially interesting items to purchase. However, people are often faced with an unpleasant situation, where the recommended items are simply the ones similar to what they have purchased previously. One of the main reasons is that existing recommender systems in e-commerce mainly utilize primary implicit feedback (i.e., purchase history) for recommendation. Little attention has been paid to secondary implicit feedback (e.g., viewing items, adding items to shopping cart, adding items to favorite list, etc), which captures users' potential interests that may not be reflected in their purchase history. We therefore propose a personalized recommendation approach to combine the primary and secondary implicit feedback to generate the recommendation list, which is optimized towards a Bayesian objective criterion for personalized ranking. Experiments with a large-scale real-world e-commerce dataset show that the proposed approach presents a superior performance in comparison with the state-of-the-art baselines.